Switching web analytics tools is fairly common (see Part 2).  According to a recent survey by analyst John Lovett (now at Jupiter), research suggests that 62% of “best-in-class” companies are currently on their second or third web analytics vendor platform.  An update of Eric Peterson’s Vendor Discovery Tool, due out any day now, indicates that only 43% of the Fortune 1000 have even deployed web analytics technology.  That means only 430 Fortune 1000 companies have deployed web analytics tools.  If Fortune 1000 companies are considered “best-in-class,” then, ouch, web analytics vendors could really improve customer satisfaction and retention!  My friend Robbin Steif over at LunaMetrics recently asked ”why?”  Why do companies switch web analytics tools so often?

The answer is that every company reaches a threshold moment of judgment about a particular technology - the instance the decision is made to either keep the technology or ”move on” and begin searching for new technology to replace the old.  This day of reckoning results from a combination of issues related to:

• Price
• Features
• Customer Service and Supported (covered in Part 2)
• Fulfillment (covered in Part 2)
• The Company Itself (not the vendor, us, and covered in Part 2)

The same goes for web analytic’s tools.  Challenges related to these five items fester until they cause a company to begin thinking about switching.   For example (the list continues in Part 2):

• Price:
  • Licensing or Subscription Costs.  Whether your running an in-house or hosted web analytics environment, it costs money.  Companies care more about things like the capital budgeting, debt service, and cash flow than they do about web analytics tools.  In times of limited resources or when the tool isn’t producing the desired return for a given cost, change may occur.
  • Per seat costs.  Every person who accesses the web analytics application may be thought of as having “a seat.”  How many seats do you need for your team?  your company?  The answer really depends on the scale of your implementation.  Seats aren’t inexpensive, and if you haven’t considered how many people in your organization need access the tool, you may soon find you can’t scale your implementation without significant revenue expenditures.  Per seat costs are generally factored into overall costs (sometimes as a line item).
  • Maintenance or upgrading costs.  Version upgrades or closely-related new products may not be “free.”  And that may make companies, that already feel limited with their current implementation, very ”unhappy” and cause “the switch.”
  • Costs for specific features.  Vendor’s basic pricing-models limit access to data filtering and drill-down features.  Slicing data across more than one dimension may require additional components or entirely new products, which have cost.  Some companies don’t dig deep enough during due diligence, only to buy a tool that they know can show “keywords per page” but hasn’t been configured or costs additional money to do so.   
• Features:
  • Limited features for custom reporting, segmentation, and ad/post hoc analysis by the business user.  Similar to the bullet above, Companies may offer a simple reporting tool with a limited number of ways to filter, segment, or correlate data.  To do more, the company may need to purchase additional features or completely new products not in the budget.  To lower cost, the company starts looking elsewhere for a comparable service. For example, time spent metrics are easy to calculate.  They’ve been in log files for years.  But don’t assume every page tag vendor has the ability to calculate these metrics out-of-the-box.
  • Inadequate data storage. Companies may not have asked “how long is my raw data stored” or if the data after a certain period of time.  It’s a real bummer when you want to backtest and realize you don’t have the data you need. Take for example, URL length, some companies may trim your URL’s unless you ask them not to.  You won’t know that until you realize it’s a problem.  At that point, not having historical data to test may cause managers to question the tool’s capabilities and to start thinking about the switch.
  • Limited data model, lookups, and decodes.  If the company wants deeper insights across more dimensions, the current product may have a limited data model that isn’t extensible to your business.  Decoding parameter or using lookup tables might not be possible.  The model may not enable you to track and report on things like Events, Spider/Bots, IP addresses, and file downloads.  If you can’t extend the schema, you limit your ability to use custom business dimensions already existing in your data warehouse or in other applications. 
  • Closed systems with no API’s or methods for rewriting data.  No API or methods beyond simple exporting of filetypes for getting data out of the system limits companies from achieving a 360 view of online customer experience.  If you want to do cross-channel marketing merging print and online, a simple reporting tool will not suffice (no matter how pretty the reports and trendlines). Companies that wake up to this fact, switch. 
  • Data not separate from presentation.  To integrate data across systems you need to have access to it.  For such needs, data is best in an open database, not only available via a web interface or on a “pay-to-use” basis.   Companies that wake up to this fact, switch.    

This list continues in Part 2.  Please click here to begin reading Part 2!

Add to the Conversation

Name:

Email:

Your email (required) will not be published.

Website:

Your Thoughts:

Please note that contributions are moderated and may take a little while to appear.